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Answer: Ensemble learning combines multiple models to improve overall performance. Bagging reduces variance, boosting reduces bias, and stacking combines predictions from multiple models while handling overfitting.
Ensemble learning involves combining multiple models to improve overall performance. Bagging reduces variance by training multiple models in parallel and averaging their predictions, which helps in handling overfitting. Boosting reduces bias and variance by training models sequentially, where each subsequent model tries to correct errors made by the previous model. Stacking combines predictions from multiple models, often using a meta-model, which can improve performance and handle overfitting by leveraging diverse models.
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Explain the concept of ensemble learning and provide a detailed comparison between bagging, boosting, and stacking. Discuss how each method improves model performance and handles overfitting.
A
Ensemble learning combines multiple models to improve overall performance. Bagging reduces variance, boosting reduces bias and variance, and stacking combines predictions from multiple models.
B
Ensemble learning uses a single model to improve performance. Bagging increases variance, boosting reduces bias, and stacking increases model complexity.
C
Ensemble learning is not effective in improving model performance. Bagging and boosting are similar, and stacking is used for reducing model complexity.
D
Ensemble learning combines multiple models to improve overall performance. Bagging reduces variance, boosting reduces bias, and stacking combines predictions from multiple models while handling overfitting.